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Shelf Engine Predictive Ordering System Could Reduce Fresh Food Waste

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Supermarket

Shelf Engine uses data provided by the food supplier or grocery chain – including previous orders and sales data, shelf life and gross margins – to create more accurate order lists.

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February 15, 2017 | by Sarah Hand, M.Sc.

Walk into any grocery store or supermarket and you’ll see a wide array of fresh fruits, vegetables and other perishable food products. But what happens to the fresh produce that isn’t sold? Some estimates suggest that up to 60 million tons of produce is discarded every year in the US.

That’s not only a huge amount of unnecessary food waste, but also a big expense for food suppliers and retailers. A new predictive ordering system aims to change all that by using analytics to make order lists and reduce food waste, as well as save supermarkets money.

Supermarkets currently use sales trends and a lot of guesswork to inform their product ordering strategies. However, as consumers continue to benefit from more food buying options, grocery chains must invest in smarter ordering systems to help them stay competitive.

Shelf Engine uses data provided by the food supplier or grocery chain – including previous orders and sales data, shelf life and gross margins – to create more accurate order lists. According to the entrepreneurs behind Shelf Life, the predictive analytics system is perhaps the first of its kind in the perishable food industry.



The startup recently received $800,000 in funding to further develop the technology. Initialized Capital, Founder’s Co-op and Liquid 2 Ventures were among the top investors.

“As seed investors, we’re always excited to learn about new problems that have potentially valuable software solutions — food waste is one of them,” wrote Alexis Ohanian, Co-founder, Reddit and Initialized Capital. “In addition, it's also a social problem and at a time when there are still people who go hungry, Shelf Engine is a means to reduce waste and thus cost. The food industry hasn't had the ability to solve this with software and this app helps retailers and distributors reduce their waste.”

In a case study, the developers of Shelf Engine show how the predictive ordering system helped a wholesale sandwich and snack distributer – called Molly’s – increase sales by nine percent, and total gross profits by seven percent. Molly’s began using Shelf Engine to manage orders for all of its 300 retail customers after testing the technology with just ten accounts.


Keywords: Food Waste, Perishable Food, Predictive Analytics


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